Successive-station monthly streamflow prediction using neuro-wavelet technique

Springer Science and Business Media LLC - Tập 7 Số 4 - Trang 217-229 - 2014
Ali Danandeh Mehr1, Ercan Kahya1, Farzaneh Bagheri2, Ekin Deliktas-Ozdemir3
1Istanbul Technical University, Civil Engineering Department, Istanbul, Turkey
2Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
3Faculty of Science and Letters, İstanbul Technical University, Istanbul, Turkey

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Abrahart RJ, See L (2000) Comparing neural network (NN) and auto regressive moving average (ARMA) techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14:2157–2172

Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network modelling of surface hydrology. Prog in Phys Geogr 36(4):480–513

Adamowski J (2008) Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J Hydrol 353:247–266

Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390:85–91

Addison PS, Murrary KB, Watson JN (2001) Wavelet transform analysis of open channel wake flows. J Eng Mech 127(1):58–70

Altunkaynak A (2007) Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resour Manage 21:399–408

ASCE Task committee (2000) Artificial neural networks in hydrology: hydrologic applications. J Hydrol Eng 5(2):124–137

Aytek A, Guven A, Yuce MI, Aksoy H (2008) An explicit neural network formulation for evapotranspiration. Hydrol Sci J 53(4):893–904

Besaw LE, Rizzo DM, Bierman PR, Hackett WR (2010) Advances in ungauged streamflow prediction using artificial neural networks. J Hydrol 386:27–37

Can İ, Tosunogulu F, Kahya E (2012) Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey. Water and Environ J 26:567–576

Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Phys Chem Earth, PartsA/B/C 31(18):1164–1171

Chang LC, Chang FJ, Chiang YM (2004) A two-step ahead recurrent neural network for streamflow forecasting. Hydrol Processes 18:81–92

Chang FJ, Chiang YM, Chang LC (2007) Multi-step-ahead neural networks for flood forecasting. Hydrol Sci J 52(1):114–130

Chau KW, Wu CL (2011) Hydrological predictions using data-driven models coupled with data pre-processing Lap Lambert academic publishing. Saarbrücken, Germany

Chau KW, Wu CL, Li YS (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng 10(6):485–491

Cheng CT, Chau KW, Sun YG, Lin JY (2005) Long-term prediction of discharges in Manwan Reservoir using artificial neural network models. Lect Notes Comput Sc 3498:1040–1045

Coulibaly P, Burn HD (2004) Wavelet analysis of variability in annual Canadian streamflows. Water Resour Res 40, W03105

Dahamshe A, Aksoy H (2009) Artificial neural network models for forecasting intermittent monthly precipitation in arid regions. Meteorol Appl 16:325–337

Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36(5):961–1005

De Vos NJ, Rientjes THM (2005) Constraints of artificial neural networks for rainfall–runoff modeling: trade-offs in hydrological state representation and model evaluation. Hydrol Earth Syst Sci 9:111–126

Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology. Hydrol Earth Syst Sci 14:1931–1941

Guang-Te W, Singh VP (1994) An autocorrelation function method for estimation of parameters of autoregressive models. Water Resour Manage 8:33–56

Haykin S (1999) Neural networks, a comprehensive foundation. Prentice Hall, Upper Saddle River, NJ

Hornik K, Stinchcombe M, White M (1989) Multi-layer feed forward networks are universal approximators. Neural Networks 2(5):359–366

Kakahaji H, Dehghan Banadaki H, Kakahaji A, Kakahaji A (2013) A Prediction of Urmia Lake water-level fluctuations by using analytical, linear statistic and intelligent methods. Water Resour Manage 27:4469–4492

Kisi O (2004) River flow modeling using artificial neural network. J Hydrol Eng 9(1):60–63

Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539

Kisi O (2008) Stream flow forecasting using neuro-wavelet technique. Hydrol Process 22:4142–4152

Kisi O (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng 14(8):773–782

Kisi O (2010) Wavelet regression model for short-term streamflow forecasting. J Hydrol 389:344–353

Krishna B (2013) Comparison of wavelet based ANN and Regression models for Reservoir Inflow Forecasting. J Hydrol Eng. doi: 10.1061/(ASCE)HE.1943-5584.0000892

Kücük M, Agiralioglu N (2006) Regression technique for stream flow prediction. J Appl Stat 33(9):943–960

Lin JY, Cheng CT, Sun YG, Chau KW (2005) Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models. Lect Notes Comput Sc 3612:1152–1161

Makkeasorn A, Chang NB, Zhou X (2008) Short-term streamflow forecasting with global climate change implications – A comparative study between genetic programming and neural network models. J Hydrol 352:336–354

Mallat S (1998) A Wavelet tour of signal processing, 2nd edn. Academic Press, San Diego, CA

Modarres R (2009) Multi-criteria validation of artificial neural network rainfall-runoff modeling. Hydrol Earth Syst Sci 13:411–421

Mondal MS, Wasimi SA (2006) Generating and forecasting monthly flows of the Ganjes River with PAR Model. J Hydrol 232:41–56

Muttil N, Chau KW (2006) Neural network and genetic programming for modelling coastal algal blooms. Int J Environ Pollut 28(3–4):223–238

Niu J, Sivakumar B (2013) Scale-dependent synthetic streamflow generation using a continuous wavelet transform. J Hydro 496:71–78

Nourani V, Mogaddam AA, Nadiri AO (2008) An ANN based model for spatiotemporal groundwater level forecasting. Hydrol Process 22:5054–5066

Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22(3):466–472

Nourani V, Kisi Ö, Komasi M (2011) Two hybrid Artificial Intelligence approaches for modelling rainfall–runoff process. J Hydrol 402:41–59

Nourani V, Komasi M, Alami MT (2012) Hybrid Wavelet–Genetic Programming Approach to Optimize ANN Modelling of Rainfall–Runoff Process. J Hydrol Eng 17(6):724–741

Nourani V, Hosseini Baghanam A, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243

Onderka M, Banzhaf S, Scheytt T, Krein A (2013) Seepage velocities derived from thermal records using wavelet analysis. J Hydrol 479:64–74

Ozger M (2010) Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Eng 37:1443–1451

Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212

Partal T, Küçük M (2006) Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey). Phys Chem Earth 31:1189–1200

Priddy KL, Keller PB (2005) Artificial neural networks, an introduction. SPIE Press, Washington

Principe JC, Euliano NR, Curt Lefebvre W (2000) Neural and Adaptive Systems. Wiley & Sons.

Rajaee T, Nourani V, Mohammad ZK, Kisi O (2011) River suspended sediment load prediction: application of ANN and wavelet conjunction model. J Hydrol Eng 16(8):613–627

Rathinasamy M, Khosa R (2012) Multi-scale nonlinear model for monthly streamflow forecasting: a wavelet-based approach. J Hydroinform 14(2):424–442

Rezaeianzadeh M, Stein A, Tabari H, Abghari H, Jalalkamali N, Hosseinipour EZ, Singh VP (2013a) Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. Int J Environ Sci Technol 10(6):1181–1192

Rezaeianzadeh M, Tabari H, Abghari H (2013b) Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci 6:2529–2537

Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall-runoff model using an artificial network. J Hydrol 216(4):32–55

Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 394:486–493

Sifuzzaman M, Islam MR, Ali MZ (2009) Application of wavelet transform and its advantages compared to Fourier transform. J Physical Sciences 13:121–134, www.vidyasagar.ac.in/journal

Sudheer KP, Gosain AK, Ramasastri KS (2002) A data driven algorithm for constructing artificial neural network rainfallrunoff models. Hydrol Process 16(6):1325–1330

Tahershamsi A, Majdzade Tabatabai MR, Shirkhani R (2012) An evaluation model of artificial neural network to predict stable width in gravel bed rivers. Int J Environ Sci Technol 9:333–342

Taormina R, Chau KW, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intel 25(8):1670–1676

Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306

Wu CL, Chau KW, Li YS (2009a) Methods to improve neural network performance in daily flows prediction. J Hydrol 372:80–93

Wu CL, Chau KW, Li YS (2009b) Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45, W08432

Zhou HC, Peng Y, Liang G (2008) The research of monthly discharge predictor–corrector model based on wavelet decomposition. Water Resour Manage 22(2):217–227